Zhang Wencheng, Wang Chengyi
Our autonomous driving simulation lab produces a high-precision 3D model simulating the parking lot. However, the current model still has poor rendering quality in some aspects. In this work, we develop a system to improve the rendering of the model and evaluate the quality of the rendered model.
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Evaluation and Optimization of Rendering Techniques for Autonomous Driving Simulation
为提升自动驾驶仿真场景质量，提出用离线工业渲染框架替代实时渲染，通过游戏引擎生成低质量场景并用IQA算法验证离线渲染的质量提升，改进后的场景用于训练。
Visual Perception System for Autonomous Driving
本文提出一种基于视觉的自动驾驶感知系统，集成行人等运动目标跟踪预测、相机定位与稀疏建图，并通过仿真和真实数据集验证其性能。
Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving
针对自动驾驶场景，提出HF$^2$-VAD$_{AD}$方法，从车辆自视角学习正常性表示，在罕见关键场景中实现逐像素异常检测。
Autonomous driving has been among the most popular and challenging topics in the past few years. On the road to achieving full autonomy, researchers have utilized various sensors, such as LiDAR, camera, Inertial Measurement Unit (IMU), and GPS, and developed intelligent algorithms for autonomous driving applications such as object detection, object segmentation, obstacle avoidance, and path planning. High-definition (HD) maps have drawn lots of attention in recent years. Because of the high precision and informative level of HD maps in localization, it has immediately become one of the critica…
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving
本文提出结合神经隐式表面和辐射场的多模态3D场景重建框架，融合LiDAR与相机数据，生成稠密准确的3D结构、隐式地图表示及网格，并动态过滤动态物体，为自动驾驶提供高质量场景重建。
A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving
本文提出能力驱动的MLLMs自动驾驶场景理解评估框架，从语义、空间、时间、物理四维度及情境层、模态、下游任务组织评估，并通过两个交通场景验证其适用性。
Exploring the Causality of End-to-End Autonomous Driving
本文提出一种系统性方法，通过控制变量、反事实干预和统计分析，探索端到端自动驾驶的因果性，并开发了工具和基线，在CARLA中验证，将黑箱变为白箱。
Driving in Corner Case: A Real-World Adversarial Closed-Loop Evaluation Platform for End-to-End Autonomous Driving
本文提出一个现实对抗闭环评估平台，用于生成边缘情况以评估端到端自动驾驶系统，通过真实场景图像生成与对抗交通策略结合，验证模型在极端情况下的性能。
DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving
本文提出DME-Driver自动驾驶系统，结合大型视觉语言模型作为逻辑决策器与3D场景感知模型作为控制信号生成器，并构建新数据集以实现高精度且可解释的规划。
Advancing Autonomous Driving Perception: Analysis of Sensor Fusion and Computer Vision Techniques
本报告通过分析基于视觉的系统与感知指标的最新进展，探讨了利用基于深度的感知和计算机视觉技术增强轮式自动驾驶机器人未知2D地图导航能力的方法，旨在提升自动驾驶感知系统的安全性与性能。
SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control
本文提出 SubjectDrive 模型，通过主体控制机制利用多样化外部数据源提升生成数据的多样性，从而有效扩展自动驾驶训练数据生产并持续改进下游感知模型性能。
This paper introduces the autonomous system of the "Smart Shark II" which won the Formula Student Autonomous China (FSAC) Competition in 2018. In this competition, an autonomous racecar is required to complete autonomously two laps of unknown track. In this paper, the author presents the self-driving software structure of this racecar which ensure high vehicle speed and safety. The key components ensure a stable driving of the racecar, LiDAR-based and Vision-based cone detection provide a redundant perception; the EKF-based localization offers high accuracy and high frequency state estimation;…
Autonomous driving is a multi-task problem requiring a deep understanding of the visual environment. End-to-end autonomous systems have attracted increasing interest as a method of learning to drive without exhaustively programming behaviours for different driving scenarios. When humans drive, they rely on a finely tuned sensory system which enables them to quickly acquire the information they need while filtering unnecessary details. This ability to identify task-specific high-interest regions within an image could be beneficial to autonomous driving agents and machine learning systems in gen…
Post-Training in End-to-End Autonomous Driving
探讨端到端自动驾驶中训练后处理技术，针对传统方法不足，将现有文献按监督形式分四类，阐述各分类的能力、局限与挑战，助力系统理解该领域并推动相关研究。
DrivingAgent: Design and Scheduling Agents for Autonomous Driving Systems
提出DrivingAgent框架，通过自动化模块开发（设计阶段）和强化学习训练的轻量级LLM实时调度（调度阶段），解决自动驾驶系统集成新模型和满足实时约束的挑战，在nuScenes和Bench2Drive上取得更优速度-精度权衡。
To improve the security and robustness of autonomous driving models, this paper presents SMET, a scenariobased metamorphic testing tool for autonomous driving models. The metamorphic relationship is divided into three dimensions (time, space, and event) and demonstrates its effectiveness through case studies in two types of autonomous driving models with different outputs.Experimental results show that this tool can well detect potential defects of the autonomous driving model, and complex scenes are more effective than simple scenes.
Panoptic Perception for Autonomous Driving: A Survey
本综述系统回顾了自动驾驶领域的全景感知模型，比较了其输入、架构及在性能、响应速度和资源利用率上的表现，并探讨了当前挑战与未来研究方向，为相关研究提供了重要参考。
Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehic…
OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving
提出OASim，一个基于隐式神经渲染的开放自适应自动驾驶模拟器，支持高质量场景重建、轨迹编辑和定制化数据生成，并通过感知评估验证其数据质量。
Want a Ride? Attitudes Towards Autonomous Driving and Behavior in Autonomous Vehicles
本文通过12人实地实验，发现对自动驾驶的态度并不影响驾驶干预和视线行为，指出缺乏实地测试的自动驾驶研究在评估行为、态度与接受度方面可能不可靠。
An Overview about Emerging Technologies of Autonomous Driving
本文概述了自动驾驶新兴技术，重点探讨了数据闭环框架下的关键技术和开放性问题。
Exploring the Boundaries: Thorough Software Testing for Safety-Critical Driving Scenarios Based on Kinematics in the Context of Autonomous Driving
本文研究自动驾驶中基于运动学的安全关键边角场景测试，提出运用边界值分析识别最小距离等极端条件，以提升系统鲁棒性并为相关人员提供测试指导。
Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
本文针对自动驾驶变道安全问题，提出一种基于深度学习的变道行为预测方法，旨在准确识别驾驶员变道意图，促进安全变道并提升道路安全性。
Uni-Gaussians: Unifying Camera and Lidar Simulation with Gaussians for Dynamic Driving Scenarios
针对自动驾驶多传感器仿真中NeRF类方法速度慢、高斯溅射类方法难适配非线性传感器的问题，提出Uni-Gaussians混合框架，用光栅化渲染图像、高斯光线追踪渲染LiDAR，实现了质量与效率的双重提升。
Towards Knowledge-driven Autonomous Driving
本文综述知识驱动自动驾驶技术，分析现有系统局限，提出以数据集、环境和驾驶员智能体为核心，结合大模型等AI技术构建更全面、自适应和智能的自动驾驶系统，并展望未来研究方向。
We describe the computing tasks involved in autonomous driving, examine existing autonomous driving computing platform implementations. To enable autonomous driving, the computing stack needs to simultaneously provide high performance, low power consumption, and low thermal dissipation, at low cost. We discuss possible approaches to design computing platforms that will meet these needs.
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
The infrastructure-vehicle cooperative autonomous driving approach depends on the cooperation between intelligent roads and intelligent vehicles. This approach is not only safer but also more economical compared to the traditional on-vehicle-only autonomous driving approach. In this paper, we introduce our real-world deployment experiences of cooperative autonomous driving, and delve into the details of new challenges and opportunities. Specifically, based on our progress towards commercial deployment, we follow a three-stage development roadmap of the cooperative autonomous driving approach:i…
Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
研究端到端自动驾驶模型因复杂不透明易产生不良行为的问题，核心方法是集成无监督字典学习作为可解释性模块，主要贡献为揭示决策逻辑、纠正驾驶决策以提升模型性能。
Learning from Rendering: Realistic and Controllable Extreme Rainy Image Synthesis for Autonomous Driving Simulation
针对现有降雨图像合成器光照可控性差、真实感不足的问题，本研究提出结合渲染与学习优势的合成方法，集成CARLA构建CARLARain仿真器，可生成标注配对的极端降雨图像，有效提升语义分割模型在极端雨景下的性能。
NeuRAD: Neural Rendering for Autonomous Driving
针对现有NeRF方法应用于自动驾驶时训练慢、需密集语义监督、泛化性差的问题，提出适配动态自动驾驶数据的NeuRAD新视角合成方法，经5个数据集验证性能达SOTA并将开源。
Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques
本文从人类舒适性视角综述自动驾驶架构、舒适度测量与优化技术，分析传感器天气限制及市场影响，并给出用例与未来展望。
Deep Event-based Object Detection in Autonomous Driving: A Survey
本文综述自动驾驶中基于事件相机的深度目标检测方法，分析事件数据异步稀疏带来的低延迟与轻量架构挑战，并阐明事件相机相较传统帧相机的优势。
InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving
针对真实驾驶数据中交互事件稀疏的问题，本文提出 InterHub 数据集，利用形式化方法从自然驾驶记录中提取多智能体密集交互轨迹，并提供可扩展工具包以支持自动驾驶评估与研究。
With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter wave (mmWave) radar and vision fusion is a mainstream solution for accurate obstacle detection. This article presents a detailed survey on mmWave radar and vision fusion based obstacle detection methods. First, we introduce the tasks, evaluation criteria, and datasets of object detection for autonomous driving. The process of mmWave radar and vision fusion is then divided into three parts: sensor deployment, sensor c…
In this paper, we continue our prior work on using imitation learning (IL) and model free reinforcement learning (RL) to learn driving policies for autonomous driving in urban scenarios, by introducing a model based RL method to drive the autonomous vehicle in the Carla urban driving simulator. Although IL and model free RL methods have been proved to be capable of solving lots of challenging tasks, including playing video games, robots, and, in our prior work, urban driving, the low sample efficiency of such methods greatly limits their applications on actual autonomous driving. In this work,…
Autonomous driving technology has drawn a lot of attention due to its fast development and extremely high commercial values. The recent technological leap of autonomous driving can be primarily attributed to the progress in the environment perception. Good environment perception provides accurate high-level environment information which is essential for autonomous vehicles to make safe and precise driving decisions and strategies. Moreover, such progress in accurate environment perception would not be possible without deep learning models and advanced onboard sensors, such as optical sensors (…
With the gradual maturity of 5G technology,autonomous driving technology has attracted moreand more attention among the research commu-nity. Autonomous driving vehicles rely on the co-operation of artificial intelligence, visual comput-ing, radar, monitoring equipment and GPS, whichenables computers to operate motor vehicles auto-matically and safely without human interference.However, the large-scale dataset for training andsystem evaluation is still a hot potato in the devel-opment of robust perception models. In this paper,we present the NEOLIX dataset and its applica-tions in the autonomou…
X-Driver: Explainable Autonomous Driving with Vision-Language Models
本文提出基于多模态大语言模型和思维链的X-Driver框架，通过自回归建模增强感知与决策，在CARLA闭环自动驾驶基准上超越现有最优方法并提升可解释性。
Attacking Autonomous Driving Agents with Adversarial Machine Learning: A Holistic Evaluation with the CARLA Leaderboard
该研究借助CARLA模拟器与CARLA排行榜，在不修改智能体代码的前提下测试对抗补丁对多类开源自动驾驶智能体的攻击效果，发现部分非ML模块可抵消对抗样本对ML模型的误导作用。
Results and Lessons Learned from Autonomous Driving Transportation Services in Airfield, Crowded Indoor, and Urban Environments
本文分享了在机场、拥挤室内和城市环境中开展自动驾驶运输服务的研究成果与经验教训，探讨了应对独特挑战的方法并概述了未来待解决的问题。
Human-vehicle cooperative driving has become the critical technology of autonomous driving, which reduces the workload of human drivers. However, the complex and uncertain road environments bring great challenges to the visual perception of cooperative systems. And the perception characteristics of autonomous driving differ from manual driving a lot. To enhance the visual perception capability of human-vehicle cooperative driving, this paper proposed a cooperative visual perception model. 506 images of complex road and traffic scenarios were collected as the data source. Then this paper improv…
Since deep neural networks' resurgence, reinforcement learning has gradually strengthened and surpassed humans in many conventional games. However, it is not easy to copy these accomplishments to autonomous driving because state spaces are immensely complicated in the real world and action spaces are continuous and fine control is necessary. Besides, autonomous driving systems must also maintain their functionality regardless of the environment's complexity. The deep reinforcement learning domain (DRL) has become a robust learning framework to handle complex policies in high dimensional surrou…
Recent developments in autonomous driving technology have proven that map data may be used, not only for general routing purposes, but also for to enhance and complement common sensor data. This document reviews the most commonly used interfaces and formats at each step of a selfhealing map data chain.
Applications of Large Scale Foundation Models for Autonomous Driving
本文探讨了大语言模型与基础模型在自动驾驶中的应用，旨在利用人类知识与推理突破长尾困境，涵盖仿真、世界模型、数据标注及端到端规划等技术方向。
Assessing the Criticality of Longitudinal Driving Scenarios using Time Series Data
本文提出基于纵向驾驶时间序列数据的安全指标，用于检测自动驾驶中的安全关键场景，以支持算法的测试与验证。
This paper presents a noval method that generates optimal trajectories for autonomous vehicles for in-lane driving scenarios. The method computes a trajectory using a two-phase optimization procedure. In the first phase, the optimization procedure generates a close-form driving guide line with differetiable curvatures. In the second phase, the procedure takes the driving guide line as input, and outputs dynamically feasible, jerk and time optimal trajectories for vehicles driving along the guide line. This method is especially useful for generating trajectories at curvy road where the vehicles…
Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving
本文提出首个兼容端到端规划模型的驾驶世界模型 Drive-WM，通过视角分解联合时空建模生成多视角视频，并首次将其用于多未来预测与安全驾驶规划。
In recent years, we have witnessed increasingly high performance in the field of autonomous end-to-end driving. In particular, more and more research is being done on driving in urban environments, where the car has to follow high level commands to navigate. However, few evaluations are made on the ability of these agents to react in an unexpected situation. Specifically, no evaluations are conducted on the robustness of driving agents in the event of a bad high-level command. We propose here an evaluation method, namely a benchmark that allows to assess the robustness of an agent, and to appr…
Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and class…
Adversarial Driving: Attacking End-to-End Autonomous Driving
本文针对端到端自动驾驶模型提出两种白盒定向图像扰动攻击，可在CPU上实时运行，并显著偏离预测转向角。
Autonomous driving clouds provide essential services to support autonomous vehicles. Today these services include but not limited to distributed simulation tests for new algorithm deployment, offline deep learning model training, and High-Definition (HD) map generation. These services require infrastructure support including distributed computing, distributed storage, as well as heterogeneous computing. In this paper, we present the details of how we implement a unified autonomous driving cloud infrastructure, and how we support these services on top of this infrastructure.
Deep Neural Networks (DNNs) have been widely applied in many autonomous systems such as autonomous driving. Recently, DNN testing has been intensively studied to automatically generate adversarial examples, which inject small-magnitude perturbations into inputs to test DNNs under extreme situations. While existing testing techniques prove to be effective, they mostly focus on generating digital adversarial perturbations (particularly for autonomous driving), e.g., changing image pixels, which may never happen in physical world. There is a critical missing piece in the literature on autonomous …
CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving
本文提出CoDa-4DGS方法，通过融合2D语义分割自监督的上下文特征与相邻帧时间形变跟踪，增强4D高斯泼溅对自动驾驶动态场景的渲染精度，在4D重建和新视角合成上优于其他自监督方法。
A Language Agent for Autonomous Driving
本文提出Agent-Driver，利用大型语言模型作为认知智能体，通过工具库、认知记忆和推理引擎将类人智能集成到自动驾驶系统，在nuScenes基准上大幅超越现有方法，并具备卓越可解释性和少样本学习能力。
Autonomous driving has become real; semi-autonomous driving vehicles in an affordable price range are already on the streets, and major automotive vendors are actively developing full self-driving systems to deploy them in this decade. Before rolling the products out to the end-users, it is critical to test and ensure the safety of the autonomous driving systems, consisting of multiple layers intertwined in a complicated way. However, while safety-critical bugs may exist in any layer and even across layers, relatively little attention has been given to testing the entire driving system across …
RoboCar: A Rapidly Deployable Open-Source Platform for Autonomous Driving Research
本文提出基于 2018 款 KIA Soul EV 的开源自动驾驶研究平台 RoboCar，通过模块化软硬件架构降低改装成本，并经公共道路实测验证其研究价值。
Designing or learning an autonomous driving policy is undoubtedly a challenging task as the policy has to maintain its safety in all corner cases. In order to secure safety in autonomous driving, the ability to detect hazardous situations, which can be seen as an out-of-distribution (OOD) detection problem, becomes crucial. However, most conventional datasets only provide expert driving demonstrations, although some non-expert or uncommon driving behavior data are needed to implement a safety guaranteed autonomous driving platform. To this end, we present a novel dataset called the R3 Driving …
Vision-based 3D Detection task is fundamental task for the perception of an autonomous driving system, which has peaked interest amongst many researchers and autonomous driving engineers. However achieving a rather good 3D BEV (Bird's Eye View) performance is not an easy task using 2D sensor input-data with cameras. In this paper we provide a literature survey for the existing Vision Based 3D detection methods, focused on autonomous driving. We have made detailed analysis of over $60$ papers leveraging Vision BEV detections approaches and highlighted different sub-groups for detailed understan…
DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes
DrivingGaussian提出一种复合高斯泼溅框架，通过增量静态3D高斯建模背景、复合动态高斯图重建移动目标，并结合LiDAR先验，实现了高保真、多相机一致的动态自动驾驶场景逼真环视合成。
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